Overview

Dataset statistics

Number of variables31
Number of observations217
Missing cells220
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.7 KiB
Average record size in memory541.4 B

Variable types

Categorical12
Numeric17
DateTime1
Boolean1

Dataset

DescriptionQuality-verified clinical data for JHB_WRHI_003
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Variable descriptions

study_sourceStudy identifier
Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
enrollment_dateDate of study enrollment
visit_dateDate of clinic visit
primary_datePrimary reference date
study_armStudy treatment arm
study_visitStudy visit number
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index
weight_kgBody weight in kilograms
height_mHeight in meters
Waist circumference (cm)Waist circumference in centimeters
hip_circumference_cmHip circumference in centimeters
waist_hip_ratioWaist-to-hip ratio
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate in beats per minute
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature in Celsius
CD4 cell count (cells/µL)CD4+ T lymphocyte count
HIV viral load (copies/mL)HIV RNA copies per mL
cd4_percentCD4+ percentage
cd8_count_cells_uLCD8+ T lymphocyte count
cd4_cd8_ratioCD4/CD8 ratio
Hematocrit (%)Hematocrit
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count
Neutrophil count (×10⁹/L)Neutrophil absolute count
Monocyte count (×10⁹/L)Monocyte absolute count
Eosinophil count (×10⁹/L)Eosinophil absolute count
Basophil count (×10⁹/L)Basophil absolute count
lymphocyte_percentLymphocyte percentage
neutrophil_percentNeutrophil percentage
monocyte_percentMonocyte percentage
eosinophil_percentEosinophil percentage
basophil_percentBasophil percentage
ALT (U/L)Alanine aminotransferase
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
direct_bilirubin_mg_dLDirect bilirubin
indirect_bilirubin_mg_dLIndirect bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
ggt_u_LGamma-glutamyl transferase
creatinine_umol_LSerum creatinine (µmol/L)
creatinine_mg_dLSerum creatinine (mg/dL)
creatinine clearanceEstimated creatinine clearance
bun_mg_dLBlood urea nitrogen
urea_mmol_LSerum urea
egfr_ml_minEstimated glomerular filtration rate
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
chloride_mEq_LSerum chloride
bicarbonate_mEq_LSerum bicarbonate
calcium_mg_dLSerum calcium
magnesium_mg_dLSerum magnesium
phosphate_mg_dLSerum phosphate
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
vldl_cholesterol_mg_dLVLDL cholesterol
cholesterol_hdl_ratioTotal cholesterol/HDL ratio
fasting_glucose_mmol_LFasting blood glucose (mmol/L)
glucose_mg_dLBlood glucose (mg/dL)
hba1c_percentGlycated hemoglobin
insulin_uIU_mLSerum insulin
lactate_mmol_LBlood lactate
crp_mg_LC-reactive protein
esr_mm_hrErythrocyte sedimentation rate
pt_secondsProthrombin time
inrInternational normalized ratio
aptt_secondsActivated partial thromboplastin time
uric_acid_mg_dLSerum uric acid
ldh_u_LLactate dehydrogenase
ck_u_LCreatine kinase
amylase_u_LSerum amylase
lipase_u_LSerum lipase
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_daily_min_tempDaily minimum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_day_p95Heat day indicator (>95th percentile)
climate_heat_stress_indexHeat stress index
climate_humidityRelative humidity
climate_precipitationPrecipitation
climate_seasonSeason
cd4_correction_appliedQuality flag: CD4 corrections applied
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circumference unit corrected
sa_biomarker_standardsSouth African biomarker reference standards applied

Alerts

study_source has constant value "JHB_WRHI_003"Constant
Antiretroviral Therapy Status has constant value "Positive"Constant
Albumin (g/dL) has constant value "41.0"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
sa_biomarker_standards has constant value "1.0"Constant
ALT (U/L) is highly overall correlated with AST (U/L)High correlation
AST (U/L) is highly overall correlated with ALT (U/L)High correlation
CD4 cell count (cells/µL) is highly overall correlated with White blood cell count (×10³/µL)High correlation
Sex is highly overall correlated with creatinine_umol_L and 1 other fieldsHigh correlation
White blood cell count (×10³/µL) is highly overall correlated with CD4 cell count (cells/µL)High correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 5 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_daily_max_temp and 5 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_heat_day_p90 is highly overall correlated with climate_daily_max_temp and 5 other fieldsHigh correlation
climate_heat_day_p95 is highly overall correlated with climate_daily_max_temp and 5 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_season is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_heat_day_p90 and 2 other fieldsHigh correlation
creatinine clearance is highly overall correlated with creatinine_umol_LHigh correlation
creatinine_umol_L is highly overall correlated with Sex and 1 other fieldsHigh correlation
hemoglobin_g_dL is highly overall correlated with SexHigh correlation
Race is highly imbalanced (95.8%)Imbalance
HIV viral load (copies/mL) is highly imbalanced (61.9%)Imbalance
CD4 cell count (cells/µL) has 4 (1.8%) missing valuesMissing
Albumin (g/dL) has 216 (99.5%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:11:27.919990
Analysis finished2025-11-25 05:11:37.516520
Duration9.6 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Study identifier

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
JHB_WRHI_003
217 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2604
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_WRHI_003
2nd rowJHB_WRHI_003
3rd rowJHB_WRHI_003
4th rowJHB_WRHI_003
5th rowJHB_WRHI_003

Common Values

ValueCountFrequency (%)
JHB_WRHI_003217
100.0%

Length

2025-11-25T07:11:37.536905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:37.569038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_wrhi_003217
100.0%

Most occurring characters

ValueCountFrequency (%)
H434
16.7%
_434
16.7%
0434
16.7%
J217
8.3%
B217
8.3%
W217
8.3%
R217
8.3%
I217
8.3%
3217
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1519
58.3%
Decimal Number651
25.0%
Connector Punctuation434
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H434
28.6%
J217
14.3%
B217
14.3%
W217
14.3%
R217
14.3%
I217
14.3%
Decimal Number
ValueCountFrequency (%)
0434
66.7%
3217
33.3%
Connector Punctuation
ValueCountFrequency (%)
_434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1519
58.3%
Common1085
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
H434
28.6%
J217
14.3%
B217
14.3%
W217
14.3%
R217
14.3%
I217
14.3%
Common
ValueCountFrequency (%)
_434
40.0%
0434
40.0%
3217
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H434
16.7%
_434
16.7%
0434
16.7%
J217
8.3%
B217
8.3%
W217
8.3%
R217
8.3%
I217
8.3%
3217
8.3%

Age (at enrolment)
Real number (ℝ)

Patient age at study enrollment

Distinct39
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.663594
Minimum20
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:37.604708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q136
median40
Q347
95-th percentile56.2
Maximum67
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0984802
Coefficient of variation (CV)0.19437786
Kurtosis-0.0090547417
Mean41.663594
Median Absolute Deviation (MAD)6
Skewness0.40369989
Sum9041
Variance65.585381
MonotonicityNot monotonic
2025-11-25T07:11:37.646204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4016
 
7.4%
3915
 
6.9%
4613
 
6.0%
3413
 
6.0%
3713
 
6.0%
3511
 
5.1%
4211
 
5.1%
3810
 
4.6%
449
 
4.1%
497
 
3.2%
Other values (29)99
45.6%
ValueCountFrequency (%)
201
 
0.5%
251
 
0.5%
262
 
0.9%
271
 
0.5%
282
 
0.9%
291
 
0.5%
307
3.2%
316
2.8%
321
 
0.5%
335
2.3%
ValueCountFrequency (%)
671
 
0.5%
631
 
0.5%
621
 
0.5%
611
 
0.5%
582
 
0.9%
575
2.3%
561
 
0.5%
554
1.8%
547
3.2%
534
1.8%

Sex
Categorical

High correlation 

Biological sex

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Female
153 
Male
64 

Length

Max length6
Median length6
Mean length5.4101382
Min length4

Characters and Unicode

Total characters1174
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female153
70.5%
Male64
29.5%

Length

2025-11-25T07:11:37.691164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:37.729387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female153
70.5%
male64
29.5%

Most occurring characters

ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter957
81.5%
Uppercase Letter217
 
18.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e370
38.7%
a217
22.7%
l217
22.7%
m153
16.0%
Uppercase Letter
ValueCountFrequency (%)
F153
70.5%
M64
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e370
31.5%
a217
18.5%
l217
18.5%
F153
13.0%
m153
13.0%
M64
 
5.5%

Race
Categorical

Imbalance 

Racial/ethnic group

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
Black
216 
Mixed Race
 
1

Length

Max length10
Median length5
Mean length5.0230415
Min length5

Characters and Unicode

Total characters1090
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowBlack
2nd rowBlack
3rd rowBlack
4th rowBlack
5th rowBlack

Common Values

ValueCountFrequency (%)
Black216
99.5%
Mixed Race1
 
0.5%

Length

2025-11-25T07:11:37.765656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:37.800246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
black216
99.1%
mixed1
 
0.5%
race1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a217
19.9%
c217
19.9%
B216
19.8%
l216
19.8%
k216
19.8%
e2
 
0.2%
M1
 
0.1%
i1
 
0.1%
x1
 
0.1%
d1
 
0.1%
Other values (2)2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter871
79.9%
Uppercase Letter218
 
20.0%
Space Separator1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a217
24.9%
c217
24.9%
l216
24.8%
k216
24.8%
e2
 
0.2%
i1
 
0.1%
x1
 
0.1%
d1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B216
99.1%
M1
 
0.5%
R1
 
0.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1089
99.9%
Common1
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a217
19.9%
c217
19.9%
B216
19.8%
l216
19.8%
k216
19.8%
e2
 
0.2%
M1
 
0.1%
i1
 
0.1%
x1
 
0.1%
d1
 
0.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a217
19.9%
c217
19.9%
B216
19.8%
l216
19.8%
k216
19.8%
e2
 
0.2%
M1
 
0.1%
i1
 
0.1%
x1
 
0.1%
d1
 
0.1%
Other values (2)2
 
0.2%

primary_date
Date

Primary reference date

Distinct112
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum2016-07-19 00:00:00
Maximum2017-06-15 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:11:37.837784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:37.885643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Antiretroviral Therapy Status
Categorical

Constant 

Current ART status

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Positive
217 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1736
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive217
100.0%

Length

2025-11-25T07:11:37.933054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:37.967931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive217
100.0%

Most occurring characters

ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1519
87.5%
Uppercase Letter217
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i434
28.6%
o217
14.3%
s217
14.3%
t217
14.3%
v217
14.3%
e217
14.3%
Uppercase Letter
ValueCountFrequency (%)
P217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1736
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i434
25.0%
P217
12.5%
o217
12.5%
s217
12.5%
t217
12.5%
v217
12.5%
e217
12.5%

CD4 cell count (cells/µL)
Real number (ℝ)

High correlation  Missing 

CD4+ T lymphocyte count

Distinct194
Distinct (%)91.1%
Missing4
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean669.23944
Minimum90
Maximum1596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.004183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile210.4
Q1496
median637
Q3885
95-th percentile1136.8
Maximum1596
Range1506
Interquartile range (IQR)389

Descriptive statistics

Standard deviation278.34576
Coefficient of variation (CV)0.41591357
Kurtosis0.1796013
Mean669.23944
Median Absolute Deviation (MAD)184
Skewness0.39364919
Sum142548
Variance77476.362
MonotonicityNot monotonic
2025-11-25T07:11:38.050382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8943
 
1.4%
5963
 
1.4%
9473
 
1.4%
6202
 
0.9%
4042
 
0.9%
5672
 
0.9%
8472
 
0.9%
6262
 
0.9%
5942
 
0.9%
6512
 
0.9%
Other values (184)190
87.6%
(Missing)4
 
1.8%
ValueCountFrequency (%)
901
0.5%
1211
0.5%
1281
0.5%
1381
0.5%
1541
0.5%
1601
0.5%
1651
0.5%
1771
0.5%
1781
0.5%
1901
0.5%
ValueCountFrequency (%)
15961
0.5%
15011
0.5%
13711
0.5%
13391
0.5%
12541
0.5%
12391
0.5%
12301
0.5%
11871
0.5%
11841
0.5%
11781
0.5%

HIV viral load (copies/mL)
Categorical

Imbalance 

HIV RNA copies per mL

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
176 
40.0
39 
41.0
 
1
63.0
 
1

Length

Max length4
Median length3
Mean length3.1889401
Min length3

Characters and Unicode

Total characters692
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row0.0
2nd row0.0
3rd row40.0
4th row0.0
5th row40.0

Common Values

ValueCountFrequency (%)
0.0176
81.1%
40.039
 
18.0%
41.01
 
0.5%
63.01
 
0.5%

Length

2025-11-25T07:11:38.096716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:38.133398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0176
81.1%
40.039
 
18.0%
41.01
 
0.5%
63.01
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number475
68.6%
Other Punctuation217
31.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0432
90.9%
440
 
8.4%
11
 
0.2%
61
 
0.2%
31
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common692
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII692
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0432
62.4%
.217
31.4%
440
 
5.8%
11
 
0.1%
61
 
0.1%
31
 
0.1%

hemoglobin_g_dL
Real number (ℝ)

High correlation 

Hemoglobin concentration

Distinct68
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.53871
Minimum7.6
Maximum17.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.255230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile10.5
Q112.5
median13.5
Q314.7
95-th percentile16.3
Maximum17.7
Range10.1
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.753429
Coefficient of variation (CV)0.12951227
Kurtosis0.35834819
Mean13.53871
Median Absolute Deviation (MAD)1.1
Skewness-0.28913593
Sum2937.9
Variance3.0745131
MonotonicityNot monotonic
2025-11-25T07:11:38.302858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1410
 
4.6%
14.19
 
4.1%
12.57
 
3.2%
12.97
 
3.2%
137
 
3.2%
13.37
 
3.2%
13.86
 
2.8%
15.56
 
2.8%
14.66
 
2.8%
13.26
 
2.8%
Other values (58)146
67.3%
ValueCountFrequency (%)
7.61
 
0.5%
8.61
 
0.5%
91
 
0.5%
9.31
 
0.5%
9.71
 
0.5%
9.91
 
0.5%
10.13
1.4%
10.41
 
0.5%
10.53
1.4%
10.61
 
0.5%
ValueCountFrequency (%)
17.71
 
0.5%
17.61
 
0.5%
17.41
 
0.5%
17.31
 
0.5%
16.93
1.4%
16.51
 
0.5%
16.41
 
0.5%
16.35
2.3%
161
 
0.5%
15.93
1.4%

White blood cell count (×10³/µL)
Real number (ℝ)

High correlation 

Total WBC count

Distinct170
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4964055
Minimum2.25
Maximum15.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.349253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.25
5-th percentile3.358
Q14.36
median5.21
Q36.5
95-th percentile8.102
Maximum15.85
Range13.6
Interquartile range (IQR)2.14

Descriptive statistics

Standard deviation1.7174402
Coefficient of variation (CV)0.31246607
Kurtosis8.8783306
Mean5.4964055
Median Absolute Deviation (MAD)0.99
Skewness1.8992561
Sum1192.72
Variance2.9496009
MonotonicityNot monotonic
2025-11-25T07:11:38.398790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.363
 
1.4%
4.453
 
1.4%
5.743
 
1.4%
5.983
 
1.4%
4.223
 
1.4%
5.212
 
0.9%
4.472
 
0.9%
4.152
 
0.9%
4.22
 
0.9%
7.482
 
0.9%
Other values (160)192
88.5%
ValueCountFrequency (%)
2.251
0.5%
2.281
0.5%
2.41
0.5%
2.481
0.5%
2.51
0.5%
2.971
0.5%
3.151
0.5%
3.171
0.5%
3.211
0.5%
3.251
0.5%
ValueCountFrequency (%)
15.851
0.5%
14.981
0.5%
8.971
0.5%
8.911
0.5%
8.641
0.5%
8.561
0.5%
8.472
0.9%
8.31
0.5%
8.211
0.5%
8.151
0.5%

Platelet count (×10³/µL)
Real number (ℝ)

Platelet count

Distinct145
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.53456
Minimum110
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.445564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile171.8
Q1219
median251
Q3306
95-th percentile385.4
Maximum588
Range478
Interquartile range (IQR)87

Descriptive statistics

Standard deviation71.369474
Coefficient of variation (CV)0.26979262
Kurtosis2.7657312
Mean264.53456
Median Absolute Deviation (MAD)44
Skewness1.1693336
Sum57404
Variance5093.6018
MonotonicityNot monotonic
2025-11-25T07:11:38.494175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2074
 
1.8%
2344
 
1.8%
2304
 
1.8%
2644
 
1.8%
2454
 
1.8%
1643
 
1.4%
2053
 
1.4%
2373
 
1.4%
2353
 
1.4%
2193
 
1.4%
Other values (135)182
83.9%
ValueCountFrequency (%)
1101
 
0.5%
1341
 
0.5%
1411
 
0.5%
1461
 
0.5%
1481
 
0.5%
1631
 
0.5%
1643
1.4%
1651
 
0.5%
1711
 
0.5%
1721
 
0.5%
ValueCountFrequency (%)
5881
0.5%
5272
0.9%
4771
0.5%
4601
0.5%
4471
0.5%
4221
0.5%
4031
0.5%
3991
0.5%
3901
0.5%
3871
0.5%

MCV (MEAN CELL VOLUME)
Real number (ℝ)

Mean corpuscular volume

Distinct166
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.740553
Minimum60.5
Maximum121.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.541081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60.5
5-th percentile77.26
Q189.3
median96.6
Q3106.4
95-th percentile112.82
Maximum121.1
Range60.6
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation11.520048
Coefficient of variation (CV)0.11908189
Kurtosis-0.11213617
Mean96.740553
Median Absolute Deviation (MAD)8.7
Skewness-0.43326315
Sum20992.7
Variance132.7115
MonotonicityNot monotonic
2025-11-25T07:11:38.589677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.54
 
1.8%
92.34
 
1.8%
111.44
 
1.8%
95.63
 
1.4%
109.73
 
1.4%
87.32
 
0.9%
99.42
 
0.9%
100.22
 
0.9%
1032
 
0.9%
932
 
0.9%
Other values (156)189
87.1%
ValueCountFrequency (%)
60.51
0.5%
62.21
0.5%
671
0.5%
68.21
0.5%
69.51
0.5%
72.31
0.5%
73.91
0.5%
75.31
0.5%
76.11
0.5%
76.71
0.5%
ValueCountFrequency (%)
121.11
0.5%
117.11
0.5%
115.71
0.5%
114.71
0.5%
1141
0.5%
113.71
0.5%
113.61
0.5%
113.41
0.5%
1131
0.5%
112.92
0.9%

RDW
Real number (ℝ)

Red cell distribution width

Distinct58
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.628111
Minimum11.9
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.637640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile12.6
Q113.5
median14.3
Q315.1
95-th percentile18.34
Maximum23.9
Range12
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.805263
Coefficient of variation (CV)0.12341054
Kurtosis5.6324967
Mean14.628111
Median Absolute Deviation (MAD)0.8
Skewness2.0358278
Sum3174.3
Variance3.2589747
MonotonicityNot monotonic
2025-11-25T07:11:38.686184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.812
 
5.5%
14.311
 
5.1%
13.310
 
4.6%
14.110
 
4.6%
14.29
 
4.1%
158
 
3.7%
13.48
 
3.7%
13.98
 
3.7%
15.28
 
3.7%
14.77
 
3.2%
Other values (48)126
58.1%
ValueCountFrequency (%)
11.91
 
0.5%
12.13
1.4%
12.21
 
0.5%
12.41
 
0.5%
12.53
1.4%
12.66
2.8%
12.71
 
0.5%
12.82
 
0.9%
12.94
1.8%
133
1.4%
ValueCountFrequency (%)
23.91
0.5%
21.91
0.5%
20.82
0.9%
20.51
0.5%
201
0.5%
19.82
0.9%
19.51
0.5%
18.91
0.5%
18.51
0.5%
18.32
0.9%

ALT (U/L)
Real number (ℝ)

High correlation 

Alanine aminotransferase

Distinct42
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.926267
Minimum6
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.731396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q114
median17
Q324
95-th percentile41
Maximum98
Range92
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.499798
Coefficient of variation (CV)0.64511255
Kurtosis14.618813
Mean20.926267
Median Absolute Deviation (MAD)5
Skewness3.3227683
Sum4541
Variance182.24454
MonotonicityNot monotonic
2025-11-25T07:11:38.775659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1517
 
7.8%
1416
 
7.4%
1715
 
6.9%
1214
 
6.5%
1613
 
6.0%
1011
 
5.1%
2111
 
5.1%
1310
 
4.6%
189
 
4.1%
208
 
3.7%
Other values (32)93
42.9%
ValueCountFrequency (%)
62
 
0.9%
71
 
0.5%
82
 
0.9%
94
 
1.8%
1011
5.1%
118
3.7%
1214
6.5%
1310
4.6%
1416
7.4%
1517
7.8%
ValueCountFrequency (%)
982
0.9%
971
0.5%
711
0.5%
701
0.5%
641
0.5%
501
0.5%
462
0.9%
431
0.5%
412
0.9%
402
0.9%

AST (U/L)
Real number (ℝ)

High correlation 

Aspartate aminotransferase

Distinct34
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.705069
Minimum10
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.817340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.8
Q118
median21
Q325
95-th percentile33.2
Maximum97
Range87
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.3258645
Coefficient of variation (CV)0.36669629
Kurtosis30.009058
Mean22.705069
Median Absolute Deviation (MAD)4
Skewness4.0488585
Sum4927
Variance69.32002
MonotonicityNot monotonic
2025-11-25T07:11:38.858003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2219
 
8.8%
2018
 
8.3%
2118
 
8.3%
1717
 
7.8%
2314
 
6.5%
1913
 
6.0%
1513
 
6.0%
1813
 
6.0%
2511
 
5.1%
2411
 
5.1%
Other values (24)70
32.3%
ValueCountFrequency (%)
101
 
0.5%
123
 
1.4%
131
 
0.5%
146
 
2.8%
1513
6.0%
168
3.7%
1717
7.8%
1813
6.0%
1913
6.0%
2018
8.3%
ValueCountFrequency (%)
971
0.5%
551
0.5%
501
0.5%
481
0.5%
431
0.5%
411
0.5%
401
0.5%
391
0.5%
381
0.5%
351
0.5%

Albumin (g/dL)
Categorical

Constant  Missing 

Serum albumin

Distinct1
Distinct (%)100.0%
Missing216
Missing (%)99.5%
Memory size13.6 KiB
41.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row41.0

Common Values

ValueCountFrequency (%)
41.01
 
0.5%
(Missing)216
99.5%

Length

2025-11-25T07:11:38.900484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:38.933682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
41.01
100.0%

Most occurring characters

ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3
75.0%
Other Punctuation1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
41
33.3%
11
33.3%
01
33.3%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
25.0%
11
25.0%
.1
25.0%
01
25.0%

creatinine_umol_L
Real number (ℝ)

High correlation 

Serum creatinine (µmol/L)

Distinct59
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.866359
Minimum31
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:38.967564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile44
Q154
median62
Q371
95-th percentile91
Maximum169
Range138
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.771975
Coefficient of variation (CV)0.24695278
Kurtosis8.6528075
Mean63.866359
Median Absolute Deviation (MAD)9
Skewness1.8734959
Sum13859
Variance248.75521
MonotonicityNot monotonic
2025-11-25T07:11:39.011387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6211
 
5.1%
5410
 
4.6%
659
 
4.1%
668
 
3.7%
488
 
3.7%
577
 
3.2%
597
 
3.2%
527
 
3.2%
587
 
3.2%
637
 
3.2%
Other values (49)136
62.7%
ValueCountFrequency (%)
311
 
0.5%
391
 
0.5%
411
 
0.5%
422
 
0.9%
433
 
1.4%
444
1.8%
455
2.3%
462
 
0.9%
473
 
1.4%
488
3.7%
ValueCountFrequency (%)
1691
0.5%
1141
0.5%
1101
0.5%
992
0.9%
981
0.5%
961
0.5%
951
0.5%
941
0.5%
921
0.5%
912
0.9%

creatinine clearance
Real number (ℝ)

High correlation 

Estimated creatinine clearance

Distinct113
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04147
Minimum40
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.058368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile77.8
Q1100
median126
Q3153
95-th percentile192
Maximum243
Range203
Interquartile range (IQR)53

Descriptive statistics

Standard deviation37.493371
Coefficient of variation (CV)0.29055287
Kurtosis-0.35583415
Mean129.04147
Median Absolute Deviation (MAD)26
Skewness0.36660724
Sum28002
Variance1405.7529
MonotonicityNot monotonic
2025-11-25T07:11:39.109192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1307
 
3.2%
1205
 
2.3%
935
 
2.3%
1275
 
2.3%
1005
 
2.3%
945
 
2.3%
905
 
2.3%
864
 
1.8%
1194
 
1.8%
1354
 
1.8%
Other values (103)168
77.4%
ValueCountFrequency (%)
401
0.5%
591
0.5%
601
0.5%
642
0.9%
652
0.9%
661
0.5%
681
0.5%
711
0.5%
771
0.5%
781
0.5%
ValueCountFrequency (%)
2431
0.5%
2251
0.5%
2171
0.5%
2091
0.5%
2051
0.5%
2031
0.5%
1992
0.9%
1981
0.5%
1971
0.5%
1961
0.5%

total_cholesterol_mg_dL
Real number (ℝ)

Total cholesterol

Distinct155
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9320276
Minimum2.82
Maximum8.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.157322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.82
5-th percentile3.43
Q14.28
median4.74
Q35.53
95-th percentile6.698
Maximum8.18
Range5.36
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.962825
Coefficient of variation (CV)0.1952189
Kurtosis-0.033836679
Mean4.9320276
Median Absolute Deviation (MAD)0.61
Skewness0.41501086
Sum1070.25
Variance0.92703198
MonotonicityNot monotonic
2025-11-25T07:11:39.205242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.584
 
1.8%
3.743
 
1.4%
4.793
 
1.4%
4.073
 
1.4%
4.963
 
1.4%
6.043
 
1.4%
4.383
 
1.4%
4.183
 
1.4%
3.693
 
1.4%
5.323
 
1.4%
Other values (145)186
85.7%
ValueCountFrequency (%)
2.821
 
0.5%
2.851
 
0.5%
2.891
 
0.5%
3.081
 
0.5%
3.191
 
0.5%
3.341
 
0.5%
3.392
0.9%
3.41
 
0.5%
3.433
1.4%
3.571
 
0.5%
ValueCountFrequency (%)
8.181
0.5%
7.341
0.5%
7.092
0.9%
6.971
0.5%
6.821
0.5%
6.811
0.5%
6.791
0.5%
6.781
0.5%
6.732
0.9%
6.691
0.5%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 corrections applied

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0.0
217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0217
100.0%

Length

2025-11-25T07:11:39.249275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:39.281696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0217
100.0%

Most occurring characters

ValueCountFrequency (%)
0434
66.7%
.217
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number434
66.7%
Other Punctuation217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0434
100.0%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0434
66.7%
.217
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0434
66.7%
.217
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1.0
217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0217
100.0%

Length

2025-11-25T07:11:39.316744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:39.350371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0217
100.0%

Most occurring characters

ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number434
66.7%
Other Punctuation217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1217
50.0%
0217
50.0%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circumference unit corrected

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
False
217 
ValueCountFrequency (%)
False217
100.0%
2025-11-25T07:11:39.378776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards applied

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1.0
217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0217
100.0%

Length

2025-11-25T07:11:39.415282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:39.449192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0217
100.0%

Most occurring characters

ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number434
66.7%
Other Punctuation217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1217
50.0%
0217
50.0%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1217
33.3%
.217
33.3%
0217
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.470991
Minimum10.663
Maximum24.189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.480472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.663
5-th percentile10.663
Q113.167
median19.417
Q320.448
95-th percentile24.189
Maximum24.189
Range13.526
Interquartile range (IQR)7.281

Descriptive statistics

Standard deviation4.1938266
Coefficient of variation (CV)0.24004515
Kurtosis-1.1589903
Mean17.470991
Median Absolute Deviation (MAD)2.389
Skewness-0.2628608
Sum3791.205
Variance17.588182
MonotonicityNot monotonic
2025-11-25T07:11:39.518474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10.66325
11.5%
19.41724
11.1%
20.44824
11.1%
17.78924
11.1%
12.62823
10.6%
21.80622
10.1%
19.73921
9.7%
24.18918
8.3%
14.30114
6.5%
13.16713
6.0%
Other values (2)9
 
4.1%
ValueCountFrequency (%)
10.66325
11.5%
11.2852
 
0.9%
12.62823
10.6%
13.16713
6.0%
14.30114
6.5%
17.78924
11.1%
18.1267
 
3.2%
19.41724
11.1%
19.73921
9.7%
20.44824
11.1%
ValueCountFrequency (%)
24.18918
8.3%
21.80622
10.1%
20.44824
11.1%
19.73921
9.7%
19.41724
11.1%
18.1267
 
3.2%
17.78924
11.1%
14.30114
6.5%
13.16713
6.0%
12.62823
10.6%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.035825
Minimum18.377
Maximum30.892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.554686image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.377
5-th percentile18.377
Q122.237
median25.312
Q327.258
95-th percentile30.892
Maximum30.892
Range12.515
Interquartile range (IQR)5.021

Descriptive statistics

Standard deviation3.8935366
Coefficient of variation (CV)0.15551861
Kurtosis-0.85522324
Mean25.035825
Median Absolute Deviation (MAD)3.075
Skewness-0.11095645
Sum5432.774
Variance15.159627
MonotonicityNot monotonic
2025-11-25T07:11:39.590910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
18.37725
11.5%
27.25824
11.1%
25.31224
11.1%
25.10424
11.1%
22.23723
10.6%
30.89222
10.1%
26.90421
9.7%
30.81818
8.3%
22.07914
6.5%
20.81913
6.0%
Other values (2)9
 
4.1%
ValueCountFrequency (%)
18.37725
11.5%
18.8122
 
0.9%
20.81913
6.0%
22.07914
6.5%
22.23723
10.6%
25.10424
11.1%
25.31224
11.1%
25.8597
 
3.2%
26.90421
9.7%
27.25824
11.1%
ValueCountFrequency (%)
30.89222
10.1%
30.81818
8.3%
27.25824
11.1%
26.90421
9.7%
25.8597
 
3.2%
25.31224
11.1%
25.10424
11.1%
22.23723
10.6%
22.07914
6.5%
20.81913
6.0%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Daily minimum temperature

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6424747
Minimum2.471
Maximum16.663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.623806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.471
5-th percentile2.471
Q17.16
median11.483
Q311.883
95-th percentile16.663
Maximum16.663
Range14.192
Interquartile range (IQR)4.723

Descriptive statistics

Standard deviation4.4395244
Coefficient of variation (CV)0.46041338
Kurtosis-0.89878889
Mean9.6424747
Median Absolute Deviation (MAD)2.906
Skewness-0.40687824
Sum2092.417
Variance19.709377
MonotonicityNot monotonic
2025-11-25T07:11:39.660109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2.79625
11.5%
11.68224
11.1%
14.38924
11.1%
11.88324
11.1%
2.47123
10.6%
11.48322
10.1%
10.18721
9.7%
16.66318
8.3%
7.1614
6.5%
8.12113
6.0%
Other values (2)9
 
4.1%
ValueCountFrequency (%)
2.47123
10.6%
2.79625
11.5%
4.5362
 
0.9%
7.1614
6.5%
8.12113
6.0%
10.18721
9.7%
10.4887
 
3.2%
11.48322
10.1%
11.68224
11.1%
11.88324
11.1%
ValueCountFrequency (%)
16.66318
8.3%
14.38924
11.1%
11.88324
11.1%
11.68224
11.1%
11.48322
10.1%
10.4887
 
3.2%
10.18721
9.7%
8.12113
6.0%
7.1614
6.5%
4.5362
 
0.9%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4110737
Minimum5.377
Maximum12.075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.695710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.377
5-th percentile5.377
Q16.838
median7.7
Q310.614
95-th percentile12.075
Maximum12.075
Range6.698
Interquartile range (IQR)3.776

Descriptive statistics

Standard deviation2.0251793
Coefficient of variation (CV)0.24077536
Kurtosis-0.95061379
Mean8.4110737
Median Absolute Deviation (MAD)1.198
Skewness0.36582967
Sum1825.203
Variance4.1013511
MonotonicityNot monotonic
2025-11-25T07:11:39.810793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.89825
11.5%
7.48524
11.1%
5.37724
11.1%
6.83824
11.1%
10.61423
10.6%
12.07522
10.1%
7.721
9.7%
10.69718
8.3%
6.54414
6.5%
7.41313
6.0%
Other values (2)9
 
4.1%
ValueCountFrequency (%)
5.37724
11.1%
6.54414
6.5%
6.83824
11.1%
7.2512
 
0.9%
7.41313
6.0%
7.48524
11.1%
7.721
9.7%
8.89825
11.5%
9.0647
 
3.2%
10.61423
10.6%
ValueCountFrequency (%)
12.07522
10.1%
10.69718
8.3%
10.61423
10.6%
9.0647
 
3.2%
8.89825
11.5%
7.721
9.7%
7.48524
11.1%
7.41313
6.0%
7.2512
 
0.9%
6.83824
11.1%

climate_heat_day_p90
Categorical

High correlation 

Heat day indicator (>90th percentile)

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0.0
177 
1.0
40 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0177
81.6%
1.040
 
18.4%

Length

2025-11-25T07:11:39.849583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:39.883991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0177
81.6%
1.040
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number434
66.7%
Other Punctuation217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0394
90.8%
140
 
9.2%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

climate_heat_day_p95
Categorical

High correlation 

Heat day indicator (>95th percentile)

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0.0
177 
1.0
40 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters651
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0177
81.6%
1.040
 
18.4%

Length

2025-11-25T07:11:39.919717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:39.954398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0177
81.6%
1.040
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number434
66.7%
Other Punctuation217
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0394
90.8%
140
 
9.2%
Other Punctuation
ValueCountFrequency (%)
.217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common651
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII651
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0394
60.5%
.217
33.3%
140
 
6.1%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct12
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.538332
Minimum13.864
Maximum23.736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-25T07:11:39.986678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.864
5-th percentile14.172
Q116.598
median21.25
Q322.279
95-th percentile23.736
Maximum23.736
Range9.872
Interquartile range (IQR)5.681

Descriptive statistics

Standard deviation3.2895619
Coefficient of variation (CV)0.16836452
Kurtosis-1.2946739
Mean19.538332
Median Absolute Deviation (MAD)2.412
Skewness-0.27754956
Sum4239.818
Variance10.821217
MonotonicityNot monotonic
2025-11-25T07:11:40.021580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
14.17225
11.5%
21.424
11.1%
22.27924
11.1%
16.59824
11.1%
18.83823
10.6%
23.73622
10.1%
23.621
9.7%
21.2518
8.3%
17.47114
6.5%
15.40813
6.0%
Other values (2)9
 
4.1%
ValueCountFrequency (%)
13.8642
 
0.9%
14.17225
11.5%
15.40813
6.0%
16.59824
11.1%
17.47114
6.5%
18.83823
10.6%
18.9547
 
3.2%
21.2518
8.3%
21.424
11.1%
22.27924
11.1%
ValueCountFrequency (%)
23.73622
10.1%
23.621
9.7%
22.27924
11.1%
21.424
11.1%
21.2518
8.3%
18.9547
 
3.2%
18.83823
10.6%
17.47114
6.5%
16.59824
11.1%
15.40813
6.0%

climate_season
Categorical

High correlation 

Season

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
Summer
63 
Spring
53 
Autumn
51 
Winter
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1302
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer63
29.0%
Spring53
24.4%
Autumn51
23.5%
Winter50
23.0%

Length

2025-11-25T07:11:40.060626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:11:40.097394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
summer63
29.0%
spring53
24.4%
autumn51
23.5%
winter50
23.0%

Most occurring characters

ValueCountFrequency (%)
m177
13.6%
r166
12.7%
u165
12.7%
n154
11.8%
S116
8.9%
e113
8.7%
i103
7.9%
t101
7.8%
p53
 
4.1%
g53
 
4.1%
Other values (2)101
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1085
83.3%
Uppercase Letter217
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m177
16.3%
r166
15.3%
u165
15.2%
n154
14.2%
e113
10.4%
i103
9.5%
t101
9.3%
p53
 
4.9%
g53
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
S116
53.5%
A51
23.5%
W50
23.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1302
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m177
13.6%
r166
12.7%
u165
12.7%
n154
11.8%
S116
8.9%
e113
8.7%
i103
7.9%
t101
7.8%
p53
 
4.1%
g53
 
4.1%
Other values (2)101
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m177
13.6%
r166
12.7%
u165
12.7%
n154
11.8%
S116
8.9%
e113
8.7%
i103
7.9%
t101
7.8%
p53
 
4.1%
g53
 
4.1%
Other values (2)101
7.8%

Interactions

2025-11-25T07:11:36.752586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.198243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.826697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.354808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.846989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T07:11:37.106007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.679529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.197067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.699634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.213551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.798853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.314065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.813658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.292693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.845783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.349698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.873063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.459477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.971960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.482178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.996900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.595965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:37.136416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.709859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.229620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.730394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.246407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.831116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.345851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.842812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.321747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.876983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.380693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.905299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.490256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.004478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.513181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.028663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.629492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:37.166683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.740199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.261341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.759350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.278725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.861694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.375678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.872910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.430615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.905071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.411951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.937665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.519079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.034385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.543167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.059401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.659604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:37.197106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.769728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.292918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.789223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.309572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.894285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.406559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.902494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.459575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.934774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.442396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.970467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.549960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.066503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.573114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.089690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.692412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:37.228411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:28.799394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.324983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:29.818581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.418998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:30.923966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.437277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:31.931911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.487857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:32.963896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:33.472169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.002523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:34.578089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.096608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:35.604936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.120865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:11:36.721981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:11:40.136748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ALT (U/L)AST (U/L)Age (at enrolment)CD4 cell count (cells/µL)HIV viral load (copies/mL)MCV (MEAN CELL VOLUME)Platelet count (×10³/µL)RDWRaceSexWhite blood cell count (×10³/µL)climate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_seasonclimate_temp_anomalycreatinine clearancecreatinine_umol_Lhemoglobin_g_dLtotal_cholesterol_mg_dL
ALT (U/L)1.0000.737-0.0260.1220.000-0.094-0.047-0.1660.0000.0000.025-0.011-0.069-0.0230.0000.000-0.0530.0000.031-0.0330.1670.316-0.027
AST (U/L)0.7371.000-0.0110.0860.000-0.146-0.060-0.0780.0000.236-0.104-0.007-0.071-0.0290.0510.051-0.0550.0000.052-0.2360.1860.169-0.065
Age (at enrolment)-0.026-0.0111.0000.0250.1610.095-0.055-0.0990.0000.142-0.047-0.043-0.048-0.0420.0000.000-0.0410.000-0.010-0.2970.2120.0510.198
CD4 cell count (cells/µL)0.1220.0860.0251.0000.000-0.0650.2510.0540.0000.2270.6170.0230.024-0.0600.0780.0780.0740.0380.064-0.002-0.038-0.040-0.014
HIV viral load (copies/mL)0.0000.0000.1610.0001.0000.0000.2930.0000.0000.0000.0550.0000.1040.4020.0990.0990.1210.0700.1330.0000.0000.1170.000
MCV (MEAN CELL VOLUME)-0.094-0.1460.095-0.0650.0001.000-0.106-0.1420.0000.1890.008-0.087-0.035-0.0280.1360.136-0.0690.000-0.092-0.1460.0310.1960.071
Platelet count (×10³/µL)-0.047-0.060-0.0550.2510.293-0.1061.0000.1280.0000.3460.369-0.080-0.096-0.1130.0000.000-0.0430.0000.0470.252-0.278-0.3400.119
RDW-0.166-0.078-0.0990.0540.000-0.1420.1281.0000.0000.136-0.012-0.050-0.061-0.0580.0000.000-0.0690.0000.0630.113-0.191-0.483-0.103
Race0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1870.1870.0690.0000.0000.1870.0340.1990.0000.0000.0000.000
Sex0.0000.2360.1420.2270.0000.1890.3460.1360.0001.0000.2610.0000.0000.0000.0000.0000.0000.0000.0000.3090.5890.5950.012
White blood cell count (×10³/µL)0.025-0.104-0.0470.6170.0550.0080.369-0.0120.0000.2611.0000.0250.056-0.0000.0360.0360.0660.058-0.0020.132-0.1050.0180.018
climate_daily_max_temp-0.011-0.007-0.0430.0230.000-0.087-0.080-0.0500.1870.0000.0251.0000.9020.6230.9860.9860.8470.8150.3660.0140.0620.047-0.082
climate_daily_mean_temp-0.069-0.071-0.0480.0240.104-0.035-0.096-0.0610.1870.0000.0560.9021.0000.7830.9860.9860.8210.8170.1450.0320.0030.043-0.052
climate_daily_min_temp-0.023-0.029-0.042-0.0600.402-0.028-0.113-0.0580.0690.000-0.0000.6230.7831.0000.7160.7160.4000.848-0.2560.024-0.0100.023-0.066
climate_heat_day_p900.0000.0510.0000.0780.0990.1360.0000.0000.0000.0000.0360.9860.9860.7161.0000.9850.5700.4460.8160.0000.0000.1250.096
climate_heat_day_p950.0000.0510.0000.0780.0990.1360.0000.0000.0000.0000.0360.9860.9860.7160.9851.0000.5700.4460.8160.0000.0000.1250.096
climate_heat_stress_index-0.053-0.055-0.0410.0740.121-0.069-0.043-0.0690.1870.0000.0660.8470.8210.4000.5700.5701.0000.8150.1990.0430.0020.042-0.054
climate_season0.0000.0000.0000.0380.0700.0000.0000.0000.0340.0000.0580.8150.8170.8480.4460.4460.8151.0000.7340.1420.0000.0740.101
climate_temp_anomaly0.0310.052-0.0100.0640.133-0.0920.0470.0630.1990.000-0.0020.3660.145-0.2560.8160.8160.1990.7341.000-0.0090.1090.0450.032
creatinine clearance-0.033-0.236-0.297-0.0020.000-0.1460.2520.1130.0000.3090.1320.0140.0320.0240.0000.0000.0430.142-0.0091.000-0.676-0.171-0.077
creatinine_umol_L0.1670.1860.212-0.0380.0000.031-0.278-0.1910.0000.589-0.1050.0620.003-0.0100.0000.0000.0020.0000.109-0.6761.0000.4490.098
hemoglobin_g_dL0.3160.1690.051-0.0400.1170.196-0.340-0.4830.0000.5950.0180.0470.0430.0230.1250.1250.0420.0740.045-0.1710.4491.0000.150
total_cholesterol_mg_dL-0.027-0.0650.198-0.0140.0000.0710.119-0.1030.0000.0120.018-0.082-0.052-0.0660.0960.096-0.0540.1010.032-0.0770.0980.1501.000

Missing values

2025-11-25T07:11:37.283898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:11:37.421101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:11:37.494748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceAge (at enrolment)SexRaceprimary_dateAntiretroviral Therapy StatusCD4 cell count (cells/µL)HIV viral load (copies/mL)hemoglobin_g_dLWhite blood cell count (×10³/µL)Platelet count (×10³/µL)MCV (MEAN CELL VOLUME)RDWALT (U/L)AST (U/L)Albumin (g/dL)creatinine_umol_Lcreatinine clearancetotal_cholesterol_mg_dLcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
0JHB_WRHI_00330.0FemaleBlack2016-07-19Positive1020.00.010.95.21390.083.618.116.025.0NaN49.0145.06.790.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
1JHB_WRHI_00353.0FemaleBlack2016-07-19Positive446.00.013.53.68234.0112.816.18.015.0NaN74.065.04.930.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
2JHB_WRHI_00336.0FemaleBlack2016-07-19Positive1054.040.013.37.71344.081.513.217.017.0NaN59.0199.05.190.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
3JHB_WRHI_00347.0FemaleBlack2016-07-19Positive989.00.010.86.35257.0107.215.511.012.0NaN53.0181.06.690.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
4JHB_WRHI_00334.0MaleBlack2016-07-19Positive160.040.011.24.17343.0102.015.041.032.0NaN66.0145.03.190.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
5JHB_WRHI_00340.0FemaleBlack2016-07-21Positive989.040.015.17.09319.0101.214.114.017.0NaN52.0177.05.580.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
6JHB_WRHI_00335.0MaleBlack2016-07-19Positive453.00.017.44.66229.094.913.730.024.0NaN98.0113.05.520.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
7JHB_WRHI_00330.0MaleBlack2016-07-21Positive288.00.017.74.12240.087.513.864.043.0NaN81.084.04.560.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
8JHB_WRHI_00344.0FemaleBlack2016-07-22Positive907.00.012.34.77230.092.915.025.031.0NaN74.0119.04.120.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
9JHB_WRHI_00336.0MaleBlack2016-07-25Positive509.040.015.54.72186.097.112.928.026.0NaN59.0154.04.260.01.0False1.010.66318.3772.7968.8980.00.014.172Winter
study_sourceAge (at enrolment)SexRaceprimary_dateAntiretroviral Therapy StatusCD4 cell count (cells/µL)HIV viral load (copies/mL)hemoglobin_g_dLWhite blood cell count (×10³/µL)Platelet count (×10³/µL)MCV (MEAN CELL VOLUME)RDWALT (U/L)AST (U/L)Albumin (g/dL)creatinine_umol_Lcreatinine clearancetotal_cholesterol_mg_dLcd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
207JHB_WRHI_00346.0MaleBlack2017-05-10Positive190.040.014.34.36225.086.315.340.030.0NaN62.0106.04.620.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
208JHB_WRHI_00333.0MaleBlack2017-05-22Positive596.00.015.54.45237.084.615.297.050.0NaN89.0131.06.040.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
209JHB_WRHI_00343.0FemaleBlack2017-05-18Positive541.040.012.84.58403.0113.615.227.020.0NaN45.0177.06.240.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
210JHB_WRHI_00342.0MaleBlack2017-05-17Positive530.00.017.66.31245.0111.414.339.022.0NaN81.0119.06.730.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
211JHB_WRHI_00339.0FemaleBlack2017-05-16Positive390.00.011.64.29254.0102.920.014.014.0NaN44.0172.04.460.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
212JHB_WRHI_00339.0MaleBlack2017-05-22Positive415.00.014.04.42192.084.814.218.025.0NaN62.0109.04.710.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
213JHB_WRHI_00357.0FemaleBlack2017-06-06Positive786.063.011.38.15447.089.213.816.017.0NaN71.0120.06.030.01.0False1.011.28518.8124.5367.2510.00.013.864Winter
214JHB_WRHI_00361.0MaleBlack2017-05-25Positive672.00.016.36.04320.0110.015.413.021.0NaN60.097.04.330.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
215JHB_WRHI_00337.0FemaleBlack2017-05-26Positive520.00.012.04.22248.0101.013.816.022.0NaN48.0127.05.840.01.0False1.013.16720.8198.1217.4130.00.015.408Autumn
216JHB_WRHI_00339.0FemaleBlack2017-06-15Positive888.040.012.94.22210.090.013.946.032.0NaN66.090.06.250.01.0False1.011.28518.8124.5367.2510.00.013.864Winter